Systems, methods, and apparatuses for ocular measurements

ABSTRACT

Systems, methods and apparatuses are provided for the measurement of intraocular pressure. These systems, methods and apparatuses can include an imaging apparatus for capturing two- or three-dimensional images or video of a patient&#39;s eye. An image reconstruction based on the captured images or video can be performed, and measurements can be taken of blood vessel features, curvature metrics, or distances between point pairs. In some embodiments, blood pressure measurements can also be taken synchronously with the captured images or video. From these measurements, a relationship between certain medical condition (e.g., elevated intraocular pressure, heart arrhythmia) and the extracted metrics can be established.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 62/833,478, filed Apr. 12, 2019, and to U.S. Provisional ApplicationSer. No. 62/950,753, filed Dec. 19, 2019, both of which are herebyexpressly incorporated by reference in their entireties for allpurposes.

FIELD OF INVENTION

The subject matter described herein relates to systems, methods, andapparatuses for the measurement of one or more eye characteristics toenable medical diagnosis.

BACKGROUND

The eye is a complex and vital organ and it can be susceptible to manydiseases such as, but not limited to, age-related macular degeneration,retinal pigment epithelial, retinal vein occlusion, and glaucoma. Theeye is also connected to other bodily systems such as, but not limitedto, the vascular system. Thus, it is essential that variouscharacteristics of the eye are measured and monitored such that theonset of various diseases of the eye and heart (e.g., arrythmia) can bedetected.

As an example, glaucoma is a complex disease in which the build-up offluid in the eye causes damage to the optic nerve, afflictsapproximately two million people in the United States, and many moremillions globally. Left untreated, glaucoma can result in eye pain,blurred vision, nausea, and irreversible vision loss, amongst otheradverse conditions. It is the second-leading cause of blindnessworldwide, and the leading cause of blindness in people over sixty yearsold.

If diagnosed and treated early enough, however, it is possible to slowor stop the progression of glaucoma and prevent blindness. An importantrisk factor associated with glaucoma is an elevated intraocular pressure(IOP). In this respect, monitoring IOP is an important task that iscrucial to inform patients and doctors about stages of the disease, aswell as methods to be used for intervention and therapeutic approaches.

Known IOP measurement techniques are invasive and usually include directcontact with the sclera of the eye, either by touching it with anapplicator or by using air jets to create local pressure. Theseapproaches are aimed at trying to create a physical depression in theeye that can be measured and used to estimate IOP through pre-determineddata for a given age and gender. These invasive approaches, especiallywhen performed repeatedly, often require the eye to be anesthetized andmay lead to undetected injuries while the eye is under anesthesia.Additionally, there are general assumptions about the sclera'sgeometrical and mechanical properties that may not necessarily apply toa given patient. In this respect, large variations in measuring IOP in aclinical setting using these known approaches is expected.

Accordingly, there is a present need for systems, methods, and/orapparatuses for non-invasively and more accurately measuring ocularcharacteristics/properties for use in the diagnosis of at least eye andvascular diseases.

SUMMARY

Described herein are example embodiments of systems, methods, andapparatuses for performing contactless measurement of one or morecharacteristics of a patient's eye used to diagnosis a potential medicalcondition. One of the systems can include: a light source configured toilluminate the eye; a 3D-camera assembly configured to capture aplurality of images of the eye; a 3D-reconstruction module configured togenerate a 3D model of the eye based at least on the plurality ofimages; and a data analytic module configured to determine one or morecharacteristics of the eye based at least on the 3D model. Each of theplurality of images can have depth information.

The one or more characteristics of the eye can include one or more ofblood vessel features, curvature metrics of a sclera of the eye,volumetric pulsations of the eye, total eye volume, deformations,relative local displacements over time, and radius of the eye.

The system can further include an intraocular pressure (IOP) diagnosticmodule configured to determine the IOP within the eye based at least onvolumetric pulsations of the eye and/or a rate of change of deformationof the eye over time.

In some embodiments, the system can also include a blood pressuremonitoring apparatus to synchronously measure the patient's bloodpressure. In this embodiment, the IOP diagnostic module can determinethe IOP within the eye based at least on the patient's blood pressureand the volumetric pulsation of the eye.

The system can also include a heart monitor configured to obtain heartdata of the patient's heart and a diagnostic module configured to flag apotential medical condition based at least on (a) deformations of theeye and the heart data, (b) relative local displacements over time andheart data, OR (c) variation in the relative local displacements overtime and heart data.

The diagnostic module can determine pulsatile ocular blood flow (POBF)based at least on the volumetric pulsations of the eye. The 3D-cameraassembly can be a plurality of cameras in stereoscopic alignment, one ormore cameras that can capture a plurality of images at differentfocuses, one or more off-axis cameras with Scheimpflug angles, or atelecentric camera and an off-axis camera. Each of the camera can be ahigh-speed camera that can capture images at a frame rate between30-5000 frames per second. The light source can have a wavelength with arange between 350 and 450 nm to avoid the peak of the blue light hazard.In some embodiments, the light source can have a wavelength of 400 nm.

A second system for identifying potential medical conditions usingcontactless measurement of one or more characteristics of a patient'seye is also disclosed. The second system includes: a light sourceconfigured to illuminate the eye; a 3D-camera assembly configured tocapture a plurality of images of the eye, wherein the plurality ofimages comprises depth information; a non-transitory memory; and one ormore processors. The non-transitory memory can store instructions that,when executed by one or more processors, cause the one or moreprocessors to: generate a 3D model of a portion of the eye based atleast on the plurality of images; determine one or more characteristicsof the eye based at least on the 3D model, wherein the one or morecharacteristics comprise one or more of blood vessel features (e.g.,patterns), curvature metrics of a sclera of the eye, volumetricpulsations of the eye, total eye volume, deformations, relative localdisplacements over time, and radius of the eye; and identify a potentialmedical condition based at least on the one or more characteristics ofthe eye. The 3D model can contain more information than a mesh ortexture such as, but not limited to, a model of multiple portions of theeye, a model of one or more blood vessels, or super-resolvedrepresentation of portions of the eye with associated probabilitydistributions.

A method for performing contactless measurement of one or morecharacteristics of a patient's eye to diagnosis a potential medicalcondition is also disclosed. The method includes: illuminating the eyewith light; capturing a plurality of images of the eye having depthinformation; reconstructing a stereo model of the eye based at least onthe plurality of images; and determining one or more characteristics ofthe eye based at least on the stereo model of the eye.

The one or more characteristics of the eye can include one or more ofblood vessel patterns, curvature metrics of a sclera of the eye,volumetric pulsations of the eye, total eye volume, deformations,relative local displacements over time, and radius of the eye.

The method can further include determining an intraocular pressure ofthe eye based at least on a volumetric pulsations of the eye, which canbe the rate of change of deformation of the eye over time.

The method can also include obtaining heart data of the patient's heartand identifying a potential medical condition based at least on (a)deformations of the eye and the heart data, (b) relative localdisplacements over time and heart data, OR (c) variation in the relativelocal displacements over time and heart data.

Other systems, devices, methods, features and advantages of the subjectmatter described herein will be or will become apparent to one withskill in the art upon examination of the following figures and detaileddescription. It is intended that all such additional systems, devices,methods, features and advantages be included within this description, bewithin the scope of the subject matter described herein, and beprotected by the accompanying claims. In no way should the features ofthe example embodiments be construed as limiting the appended claims,absent express recitation of those features in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The details of the subject matter set forth herein, both as to itsstructure and operation, may be apparent by study of the accompanyingfigures, in which like reference numerals refer to like parts. Thecomponents in the figures are not necessarily to scale, emphasis insteadbeing placed upon illustrating the principles of the subject matter.Moreover, all illustrations are intended to convey concepts, whererelative sizes, shapes and other detailed attributes may be illustratedschematically rather than literally or precisely.

FIG. 1 is a diagrammatic overview of an example embodiment of a systemfor performing ocular measurements.

FIGS. 2A and 2B are a photograph and a perspective view, respectively,of an example embodiment of an imaging apparatus for use in a system formeasuring ocular properties.

FIGS. 3A and 3B illustrate simple proof of concept viscoelastic model ofa human eye in accordance with example embodiments of the presentdisclosure.

FIGS. 4A and 4B illustrate ocular pulse diagrams in accordance withexample embodiments of the present disclosure.

FIG. 5 illustrates a process for ocular measurements in accordance withexample embodiments of the present disclosure.

FIG. 6A to 6C are flow chart diagrams depicting example embodiments ofmethods for ocular measurements.

FIG. 6D is a flow chart diagram depicting an example embodiment of amethod for medical diagnosis utilizing ocular properties.

FIGS. 7A and 7B are a perspective view and a photograph, respectively,of a model used for testing an example embodiment of a system formeasuring ocular properties.

FIGS. 7C to 7E are graphs showing various results from testing of amodel by an example embodiment of a system for measuring ocularproperties.

FIG. 8A to 8D are a photographic image and 3D-reconstruction of asubject's eye utilized for testing an example embodiment of a system formeasuring ocular properties.

FIGS. 8E and 8F are 3D reconstructed images based on testing of anexample embodiment of a system for measuring ocular properties.

FIGS. 8G and 8H are graphs depicting curvature measurements based ontesting of an example embodiment of a system for measuring ocularproperties.

FIG. 9A is a photograph of a model used for testing an exampleembodiment of a system for measuring ocular properties.

FIGS. 9B and 9C are graphs depicting measured scale versus relativepressure as determined during testing of an example embodiment of asystem for measuring ocular properties.

FIG. 10 pictorially illustrates a process for measuring pulsatile ocularblood flow in accordance with example embodiments of the presentdisclosure.

FIG. 11 is a block diagram depicting an example embodiment of acomputing device for use in a system for measuring ocular properties.

FIG. 12 is a block diagram depicting an example embodiment of a remoteserver for use in a system for measuring ocular properties.

FIG. 13 is a block diagram depicting an example embodiment of a mobilecomputing device for use in a system for measuring ocular properties.

DETAILED DESCRIPTION

Before the present subject matter is described in detail, it is to beunderstood that this disclosure is not limited to the particularembodiments described, as such may, of course, vary. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting, since the scope of the present disclosure will be limited onlyby the appended claims.

As used herein and in the appended claims, the singular forms “a,” “an,”and “the” include plural referents unless the context clearly dictatesotherwise.

Overview & Applications

Current diagnostic methodologies such as contact tonometry use aone-time measurement to diagnose a patient's eye condition. Since thetonometry is a contact procedure, its measurements are assumed to stayconstant for each patient. This absolute treatment of eye measurementsis wrong and can lead to incorrect diagnoses. For example, conventionalmethodologies are implemented to facilitate diagnostic procedures for alarge groups of people. However, for many degenerative diseases, theprogression of certain eye characteristics (e.g., deformation, ocularflow) over time is a more accurate parameter for monitoring anddiagnosing potential eye diseases. Unfortunately, conventionalmethodologies are limited by their inherent invasiveness and inaccuracy.Currently, most diagnostic devices require a trained ophthalmologist toperform the procedure and have scarce repeatability even within the samevisit. Accordingly, what is needed is a non-invasive (e.g.,contactless), accurate, and repeatable ocular measurement method. Thedisclosed systems and methods for performing contactless measurement ofone or more characteristics of a patient's eye allow doctors to comparemeasurements at different instances in time for an individual patient.In this way, a progressive understanding of the disease can beefficiently and accurately created. Since each patient's eyecharacteristics are unique, the ability to measure and track variouseye's characteristics over time will lead to a more accurate and timelydiagnosis.

The eye is a viscoelastic quasi-spherical structure with a pulsatileinflow and a constant outflow of blood. The fluctuating net flow ofblood in the eye creates periodic volumetric expansions and contractionscorresponding to positive and negative net flow patterns, respectively.For each cardiac cycle, the eye undergoes radial expansions andcontractions that reflect the net inflow and outflow of blood from theeye, respectively. Given the incompressibility of blood, volumetricfluctuations in the eye are directly related to the quantity of bloodentering the eye. Additionally, during an ocular radial expansion, theexpansion of certain portion of the eye (e.g., sclera) is mainly drivenby an incoming pressure wave. During an ocular radial contraction, thecontraction behavior is mainly determined the by material properties ofthe sclera. For example, tension forces of the ocular wall can causepassive recoil that drive the blood outflow. Accordingly, the ocularproperties (e.g., sclera elasticity, size) can be extracted fromanalysis of the shape, slope, and frequencies of the ocular pulsations,which dictate the ocular response to the incoming pressure wave.

By exploiting the observable pulsatile behavior and other attributes ofthe eye, the disclosed systems and methods can measure local curvatureof the eye over-time and reconstruct the ocular volumetric pulsations.This allows net flow profiles in the eye to be measured. In someembodiments, high-speed imaging system, coupled with image analysisalgorithms, can be employed to measure various characteristics of theeye such as, but not limited to, blood vessel patterns, curvaturemetrics of a sclera of the eye, total eye volume, deformations, relativelocal displacements over time, and radius of the eye. One or more ofthese characteristics can be used to generate and/or measure a 3D bloodvessel model, bulk 3D shape of the eye, local and/or regional bloodoutflow measurements, pulse pressure waveform of the blood flow,waveform propagation, waveform variation, and time based observation ofthe pulse pressure waveforms, blood vessels characteristics,deformations, and physical properties of the sclera.

In some embodiments, the disclosed systems and methods can use keypointsextraction and matching (e.g., ORB, SIFT, SURF, or neuralnetwork-derived features), template matching, and/or pattern recognitionto identify areas on the sclera that can be tracked by an imagingapparatus (e.g., one or more cameras) to generate one or more of theabove eye characteristics over time or at an instance of time. Based atleast on this information, relative IOP changes can be determinedwithout the need for repeated invasive techniques.

The disclosed systems and methods for performing contactless measurementof one or more characteristics of the eye can also be used fornon-invasive observation of the eye's pulse pressure wave. Asextensively documented in the cardiovascular literature, pulse pressurewaveforms carry information regarding the cardiovascular system'shealth. For this reason, measuring ocular radial pulsations provides adirect means of extracting pulse pressure wave shape information. Forexample, it has been observed that abnormalities in the measured ocularradial pulsations generally corresponds to abnormality in the pulsepressure wave. Accordingly, in some embodiments, the systems and methodscan “non-contactly” (e.g., contactless) measure and use ocularproperties (e.g., radial pulsation) to extract information regarding thecardiovascular system.

Additionally, an understanding of a patient's heart activity and healthcan be developed by analyzing the pulse pressure waveform over anextended period. A healthy heart functions with a consistent rhythmicpattern—accelerating and decelerating its pace to accommodate the body'soxygen demands. Multiple heart conditions affect this rhythmic patternof the heart resulting in inefficient heart function and greater risksof heart failure. As the ocular radial pulsations are reflective of thecardiac activity, these can be used to directly measure the heartrhythmic patterns. Accordingly, in some embodiments, the systems andmethods can non-contactly measure the ocular radial pulsations for aperiod of time (greater than one pulsation) to assess the heart'srhythmic function and health.

In some embodiments, the disclosed systems and methods for performingcontactless measurement of one or more characteristics of the eye can beused to diagnosis or identify potential eye conditions such as, but notlimited to, diabetic retinopathy, macular degeneration, or retinal veinocclusion. Take diabetic retinopathy as an example, it is a diabetescomplication that affects the vasculature of the eyes. During the earlystages of diabetic retinopathy, the persistence of hyperglycemiadegenerates the retinal vasculature of the eye leading to hemorrhaging,aneurysms and hard exudates. As the disease progresses, the damaged eyevasculature induces the formation of new blood vessels in a processknown as neovascularization. This process is rapid and unorganized,affecting the regular ocular flow regimes. Elucidating the fluiddynamics involved in this process can motivate the application of ocularblood flows measurements as an early diagnostic tool.

At the macroscopic scale, the vasculature changes in the eye affect theflow resistance directly influencing ocular blood flows. In the earlystages of the disease, the ocular blood flow is reduced as hemorrhaging,aneurysms and hard exudates increase flow resistances. However, as thedisease progresses, increased vasculature reduces flow resistance andthereby inducing increased flow. Additionally, pulsatile ocular bloodflow (POBF), which can be an indicator of the progression of diabeticretinopathy, can be measured by measuring and tracking the amount ofblood entering the eye during a given period of time (e.g., a beat, asecond, a minute). Previous studies have demonstrated that the POBFincreased with disease severity. Accordingly, by monitoring and trackingthe ocular blood flow (e.g., POBF) using the disclosed non-invasive eyemeasurement technique, diabetic retinopathy can be potentiallydiagnosed. Further discussion of POBF measurement is provided below.

In some embodiments, the disclosed systems and methods utilize surfacecurvature measurements to estimate ocular blood flows. Here, fluctuatingflow profiles of the eye are measured by analyzing the radial pulsatilebehavior of the ocular shell. In this way, the hemodynamic behaviors andhealth of the eye can be directly analyzed. Since hemodynamic changesprecede ocular damage, the disclosed systems and methods for measuringcharacteristics of the eye to measure and monitor hemodynamic behaviorscan be used to identify potential ocular issues.

Another diagnostic application of the disclosed systems and method isfor diagnosing age-related macular degeneration (AMD), which is adegenerative disease that damages the macular region leading toblindness. The early stages of the disease are characterized with thedeposition of insoluble extracellular aggregates called drusen in theretina. During the progression of the disease, the choroid vasculardensity is highly reduced and choriocapillaris become vasoconstricted.AMD can progress in two distinct forms: the non-exudative and exudativeAMD. Non-exudative AMD, or dry AMD, is a progression of the early stageAMD with a degeneration of the retinal pigment epithelial (RPE) layer.The late stage dry AMD, called geographic atrophy, results in largeareas of degenerated RPE.

The exudative (wet) AMD is characterized by choroidal neovascularizationtowards the outer retina. The onset of this disease is very rapid andcan lead to blindness if left untreated. Elevated choroid vasculardensity reduction during disease progression is believed to be one ofthe major factors for transition to the wet stage of AMD. Previousmedical studies have analyzed the choroidal blood flow as measured withPOBF during AMD disease progression. Previous studies showed that theprogression of the disease was marked with a decrease in POBF, and thatdecreased POBF may be a risk factor for choroidal neovascularization.Accordingly, by monitoring and tracking the patient's POBF using thedisclosed non-invasive eye measurement technique, AMD can be potentiallydiagnosed.

Another diagnostic application of the disclosed systems and method isfor diagnosing Retinal Vein Occlusion (RVO), which is a medicalcondition where the retinal veins develop a small clot that reduces orblocks the vein outflow. When vein outflow is decreased, severalconditions can develop in the eye leading to blindness if leftuntreated. These include macular edema, retinal neovascularization, andglaucoma. The blocked outflow from the eye causes a buildup of fluidthat increases vascular resistance and decreases nutrient supply. InRVO, decreased retinal blood outflow is typically compensated with amore than proportionate increase in choroidal blood flow. This mechanismis used to maintain normal O2 oxygen levels in the retina and given thegreater diffusion distance between the choroid and the inner retinagreater O2 concentrations are required. Several studies have analyzedthis phenomenon and concluded that that POBF is indeed increased in theeye having RVO. Additionally, it was shown that elevated POBF werepresent in the eye affect by branch RVO when compared to the other eyein the patient and the eyes of the control group. Accordingly, bymonitoring and tracking the patient's POBF using the disclosednon-invasive eye measurement technique, RVO can be potentiallydiagnosed.

Another diagnostic application of the disclosed systems and method isfor determining the effectiveness of antiVEGF therapy. During theprogression of multiple ocular diseases, the vascular architecture isaltered due to rapid and unorganized neovascularization. Thisuncontrolled growth of new blood vessels damages the neurons in theretina leading to blindness. Therapy for these sets of conditions isachieved through laser photocoagulation therapy or anti-vascularendothelial growth factor (antiVEGF) injections. Fundamentally, it canbe reasoned that reduction in the overall ocular vascular volume resultsin increased vascular resistance. As such, the overall ocular blood flowis expected to decrease in response to an effective therapy.

As previously discussed, DR is an ocular condition in diabetic peoplethat affects the ocular vasculature. Late stages of DR are characterizedby rapid neovascularization that detaches the retinal layer resulting inblindness. One of the treatment options available is laserphotocoagulation therapy that aims to block the growth of these vessels.Studies have shown that effective laser photocoagulation therapy candecrease the POBF in patients with proliferative DR. Accordingly, bymonitoring and tracking the patient's ocular blood flow using thedisclosed non-invasive eye measurement technique, the effectiveness oftherapies such as antiVEGF and laser photocoagulation therapy can bemonitored.

Another diagnostic application of the disclosed systems and method isfor diagnosing/detecting early onset of carotid artery stenosis (CAS).It is known that cardiovascular pressure waveforms can provideinformation regarding the entire system from the point of measurementtill the source of the wave. Eyes are supplied blood by the carotidarteries respective to their side of the body. The carotid arteriesdetach and separate from the arch of the aorta and as such there is justa small section of overlap between the left and right carotid artery.Given the substantial symmetry of the carotid structure, in a healthyindividual the amount of blood supplied by each carotid artery isequivalent. Under the circumstance that an asymmetry develops in one ofthe arteries, a noticeable pulse waveform difference will become presentover time between the two eyes.

CAS is caused by the deposition of plaque over time in the carotidartery resulting in reduced blood flow to the brain and eyes. Typically,this condition is asymmetric and occurs only in one of the two carotidarteries. The biggest complication with this disease is the symptom freeprogression until a short episode of blood flow interruption occurs,known as transient ischemic stroke. In some embodiments, ocular radialpulsations in both eyes can be compared over multiple measurements tohighlight critical differences in the pulse amplitude, which can be anindicator for CAS. Accordingly, by monitoring and tracking the patient'socular radial pulsations in both eyes using the disclosed non-invasiveeye measurement technique using the disclosed non-invasive eyemeasurement technique, early detection of CAS can be accomplished.

Another diagnostic application of the disclosed systems and method isfor measuring and monitoring 3D motion features of the eye to diagnoseeye diseases such as keratoconos and staphyloma. The propagation ofpressure waves is a phenomenon observed throughout the entirecardiovascular system. Pressure waves are generated from the heart andpropagate at a speed that is determined by the fluid's density andconfining material's structural properties. In cardiology, it has beenwell recognized that measuring the pulse wave velocity (PWV) is anindication of cardiovascular rigidity. As the eye is connected to thecardiovascular system and undergoes pulsations during each heartbeat, itcan be expected that pressure wave also propagate at the ocular level.These pressure wave propagations are reflective of the fluctuatingintraocular pressure with the incoming pulsatile from each cardiaccycle. The speed of wave propagation will be dependent on the materialproperties of the sclera. In some embodiments, the disclosed systems andmethods use the body generated impulses to observe waves on the scleralsurface in order to measure the material properties of the sclera.

Alternatively, scleral material properties can also be measured throughexternal generation of waves on the surface of the eye. Waves can begenerated by using a vibrating probe on the eyelid section overlayingthe sclera. Contacting the eye on a skin covered section allows toperform the entire procedure painlessly. In some embodiments,non-invasive surface curvature measurements can be combined with wavedata obtained from a vibrating probe to measure the scleral elasticity.

When a disturbance propagates through an elastic medium, the disturbancecan be represented by the passage of three waves. First, a longitudinalor pressure wave, which oscillates in the direction of motion. Second, atransverse or shear wave, which oscillates perpendicular to thedirection of motion, but parallel to the surface. Third, a surface orRayleigh wave, which oscillates perpendicular to the direction of motionand the surface. These three waves all travel at different speeds, andtheir respective waves speeds are a function of the material propertieslike Young's modulus, Poisson's ratio, and density. Because thedisclosed systems and methods can measure the surface motion in 3D, allthree wave motions can be determined from at least the stereo model ofthe eye, which is generated using a plurality of images. Accordingly,the relationship between the wave speeds can be used to determine thematerial properties that the disturbance passed through. In other words,by measuring the 3D motion of features on the surface of the eye atsufficient spatial and temporal resolution, the material properties ofthe eye can be inferred. These properties can be used to diagnoseconditions involving changes in the strength of the eye such askeratoconos and staphyloma or to calibrate eye pressure measurements forthe individual patients' eyes.

Another diagnostic application of the disclosed systems and method isfor detecting heart related side effects. Ocular radial pulsations havebeen shown to be directly correlated to the heart's pumping activity asthe eye pulsates in sync with the heartbeat rhythm. A healthy heartfunctions with a constant pumping rhythm such that enough nutrients aresupplied to the body for correct functionality. Abnormal heart rhythmssuch as a heart beating too quickly, too slowly or with irregularintervals are reflected in ocular radial pulsations. Thus, in someembodiments, several consecutive ocular radial pulsations can bemeasured and analyzed for various cardiovascular conditions such as, butnot limited to, tachycardia, bradycardia, arrhythmias and prematurecontractions. Additionally, several medications for both ocular andnon-ocular medical conditions have shown to have side effects thataffect the heart's pumping activity. In these cases, it is important torecognize the symptoms at an early stage and intervene to avoidcomplications. Accordingly, by monitoring and tracking the patient'socular radial pulsations using the disclosed non-invasive eyemeasurement technique, various heart conditions (e.g., tachycardia,bradycardia, and arrhythmias) can be potentially detected.

Another diagnostic application of the disclosed systems and method isfor measuring vessel lumen dilation or contraction. For thisapplication, it should be noted that the vasculature throughout thehuman body actively expands or contracts its lumen in response todifferent stimuli. Both the inner and outer surface of the eye arecovered with vasculature that exhibits this active response behavior.This active modulation of vessel lumen size can be induced by medicalconditions or by molecule imbalances in our blood stream. An example ofa medical condition that affect vasculature includes diabeticretinopathy. Some examples of blood molecular imbalances can also causevasodilation including hyperglycemia and excessive alcohol intake. Insome embodiments, the disclosed systems and methods are configured toimage the sclera to highlight the vascular structure with sub-pixelaccuracy while simultaneously resolving the scale. This is notpractically possible with conventional invasive measurement systems.

Non-invasive Measurement System

FIG. 1 is a diagrammatic overview of an example embodiment of a system100 for measuring one or more characteristics of the eye that can beused to diagnosing a potential medical condition in accordance with someembodiments of the present disclosure. System 100 is shown together witha patient 10 and his or her eyes 20. System 100 includes a light source101 and imaging sub-system 102 that can capture stereographic (e.g.,depth) data of a scene. Light source 101 can be integrated on imagingsub-system 102 or it can be a separate component. Light source 101 canemit light with a wavelength having a range between 300 to 1100 nm. Insome embodiments, the light has a wavelength of 400 nm.

Imaging sub-system 102 can include one or more cameras that can capturedepth information of a scenery. For example, imaging sub-system 102 canalso include one or more RGB-D cameras, which are cameras that cancapture images with RGB color information and pixel depth information.The one or more cameras can be high-speed cameras capable of capturingimages at a high frame rate, which can range from 30 to 1000+ frames persecond. Imaging sub-system 102 can also be a light-based projectionsystem such as, but not limited to, LIDAR (light detection and ranging),structured light, and time of flight.

The one or more cameras of imaging sub-system 102 can include two ormore cameras in a stereoscopic alignment (as described with respect toFIGS. 2A and 2B), a telecentric camera with a second off-axis camera, asingle defocusing camera with two or more apertures, a general-purposedigital camera, and/or may incorporate technology described in any orall of U.S. Pat. Nos. 6,278,847, 7,612,870, 9,530,213, and 6,229,913.These patents describe suitable hardware for three-dimensional (3D)imaging using various techniques. For example, U.S. Pat. Nos. 6,278,847and 7,612,870 describe defocusing hardware and methods for determining3D or depth information based on the separation of features imagedthrough offset apertures. U.S. Pat. Nos. 9,530,213 and 6,229,913describe different hardware and methods for determining 3D or depthinformation from the relative blurring of different images havingvarying degrees of defocus (hereinafter, “blur-based imaging”). Thesefour patents are incorporated by reference herein in their entirety forall purposes. Alternatively, a stereo imaging camera system may be usedfor 3D determination.

According to another aspect of the embodiments, imaging sub-system 102can further include off-axis cameras with Scheimpflug angles configuredto give aligned focal planes, folded optics for compactness, and/orcustom optics to match the eye curvature of the focal plane. In someembodiments, for example, imaging apparatus can comprise an f-theta lensdesign and/or include high-speed camera sensors. In other embodimentsthe imaging apparatus uses a telecentric camera as one of the camerasalong with a secondary off-axis camera, where the telecentric camerawill have minimal scale change.

Imaging sub-system 102 can be communicatively coupled to a computer 112(e.g., memory and one or more processors) that can include a3D-reconstruction module (not shown), a data analytic module (notshown), and one or more diagnostic modules (not shown). These modulescan be standalone applications/modules. Alternatively, one or more ofthese modules can be combined to form an integrated module withfunctions and features of two or more of the above modules. In someembodiments, computer 112 can also be integrated with image sub-system102.

The 3D-reconstruction module of computer 112 can reconstruct a 2D or 3Dmodel of a portion of the eye (e.g., blood vessels model, sclera model)using features extracted from images captured by imaging sub-system 102.In some embodiments, the 2D model can be a scale adjusted 2D modelconfigured to capture a snapshot of the local area that is adjusted forthe relative displacement of the camera and the eye. In this way,vascular dilation can be better measured. The captured images cancontain stereo or depth data (e.g., RGB-D) at different locations of theeye and at different times. For 3D-reconstruction, images with varyingfocuses and blur rates can also be used to construct the 3D model. Insome embodiments, the 3D-reconstruction module can create key points orfeatures on images using algorithm such as, but not limited to, SIFT(scale-invariant feature transform) algorithm, SURF (speeded up robustfeature) algorithm to identify and characterize various features of theeye such as blood vessel patterns. It should be noted that a 3D modelcan be reconstructed using other types of 3D data collection method and3D data such as, but not limited to, 3D information from LIDAR andlight-field camera (e.g., Lytro). Further, consecutive or time-lapsedimages (e.g., spatially correlated images) can be combined to de-noiseand enhance the measurements. The 3D reconstructed model can containmore information than a mesh or texture, including but not limited to 3Dmodels of one or more portions of the eye, a 3D model of two or moreblood vessels in an instant of time or over multiple time steps (e.g.,short or long duration), or some other representation of the underlyingphysical data and measurement certainty.

In some embodiments, the data analytic module of computer 112 candetermine one or more characteristics of the eye such as, but notlimited to, surface curvature, eye radius, and the approximate total eyevolume based on at least the RBG-D data of the captured images and/orthe stereoscopic reconstructed model. The data analytic module can alsouse external measurements such as, but not limited to, axial length ofthe eye, pupillary distance, age, and sex to refine one or more of theeye characteristics. Using images taken in a time series (e.g., multipletime steps), data analytic module can develop a time-based model of anyeye characteristic. For example, a multiple time steps of the surfacecurvature of the eye can be generated to observe changes in the ocularvolume. In another example, changes in the eye radius or eye volume canbe observed over time using at least images taken at different times.

In some embodiments, data analytic module can track feature points bymatching the feature points across different time steps to produce timelapsed observations of the tracked feature points. Additionally, thedata analytic module can perform local spectral analysis such as thefrequency domain of the distance between two points over multiplemeasurements (e.g., 2D deformation) to infer one or more local materialproperties of the eye. The local material properties can be thestrength, elasticity, or thickness of the sclera, for example.

In some embodiments, the data analytic module can generate a bulk 3Dshape of the eye or a portion of the eye using one or more images from asingle time slice. The images can include stereo, defocusing, and/orRGBD data. In this way, the data analytic module can estimate the bulkparameters of the eye such as, but not limited to, the eye's radius,volume, and surface curvature.

In some embodiments, the data analytic module can generate deformationsdata of one or more portions of the eye. The data analytic module cantrack points of interest (e.g., blood vessels) in the eye over multipletime slices. This can provide relative deformation information inaddition to bulk 3D information. The data analytic module can alsomeasure ocular pulsation by analyzing the variation of blood flow fromheartbeat.

In some embodiments, the data analytic module can generate localfrequency or rates of change analysis, which analyzes deformations dataand over time to determine the local rates of change through spectralanalysis and/or by inferring local relative displacements.

In some embodiments, the data analytic module can generate a model ofthe local vasculature. This is because the 3D (e.g., stereoscopic)reconstructed model allows for accurate scale resolution that is not bepossible with a conventional imaging system. In this way, specific veinor capillaries over time and estimate the dilation or contraction can bemeasured and tracked.

As previously mentioned, computer 112 can include one or more diagnosticmodules (not shown), each of which is designed and programmed to utilizeone or more eye characteristics measured by the data analytic module togenerate various metrics such as, but not limited to, ocular outflow,ocular deformation, pulse waveform, 2D wave propagation, and ocularpulse waveform variation. In some embodiments, ocular outflow can bedetermined using at least the bulk 3D shape over multiple time stepsand/or using deformation data, which estimates the eye volume or changein the eye volume over a heartbeat.

In some embodiments, the diagnostic module can be programmed to generatea pulse waveform using at least the deformation data, which can be therate of change of displacement of the eye over a time period. Thediagnostic module can also be programmed to generate a 2D wavepropagation using at least local frequency analysis and/or deformationdata. The 2D wave propagation data can be used to infer variousproperties of the eye such as the elasticity (e.g., resistance to volumechanges) of the sclera.

In some embodiments, the diagnostic module can be programmed to generatewaveform variation by tracking a waveform over multiple heartbeats. Inthis embodiment, a heart monitor can be used to detect the heartbeat.

Various functions of system 100 can be implemented on or with a mobilecomputing device 110 (e.g., a smart phone or a tablet), using anincluded camera and on-board processing componentry. Alternatively,mobile computing device 110 may be used for imaging and data displayalone communicating wirelessly with the local computing device 106 orremote server 108 for additional processing resources. Alternatively,one or more modules (e.g., data analytic module, diagnostic module) ofcomputer 112 can be integrated on mobile device 110.

According to many of the embodiments, the digital image sensor(s) ofimaging apparatus 102 are communicatively coupled to computer processingcircuitry included in the system. Furthermore, non-transitory memory(variously located) provides storage for software to run the aboveprocesses/modules. IOP-related indications, diagnosis alerts,measurements or estimates that are generated from the digital imagesensor data may be output to one or more of display 122 of mobilecomputing device 110, display 112 of local computing device 106, or anyother display communicatively coupled with system 100.

According to other embodiments, imaging apparatus can capture one ormore 3D images or video of the sclera of a patient's eye (or eyes).Subsequently, a 3D image reconstruction can be performed and distancesbetween selected point pairs having similar elevations can be measured.Subsequently, a point-pair-distance-to-pressure relationship can bedetermined based on a standard ocular tonometry performed eitherprevious to or during the same session using pressure-modifying eyedrops. In a following session, the sclera can then be re-imaged and animage reconstruction can be performed again. From the subsequent imagesor image reconstruction, a second set of point pair distances can bemeasured, compared to the first set of point pair distances, and anintraocular pressure measurement can be determined based on thepoint-pair-distance-to-pressure relationship.

According to still other embodiments, curvature metrics, instead ofpoint pair distances, can be utilized to estimate IOP change. In theseembodiments, imaging apparatus can capture one or more 3D images orvideo of the sclera of the patient's eye. Subsequently, a 3D imagereconstruction can be performed. A first set of curvature metrics canthen be extracted from either the images or the 3D-reconstruction, and acurvature-pressure relationship can be determined based on a standardocular tonometry performed either previous to or during the samesession. In a following session, the sclera can be re-imaged utilizingthe imaging apparatus, and a 3D image reconstruction can be performedagain. From the subsequent images or image reconstruction, a second setof curvature metrics can be extracted, compared to the first set ofcurvature metrics, and, subsequently, an intraocular measurement can bedetermined based on the curvature-pressure relationship.

In many of the embodiments described herein, the imaging apparatus cancomprise two or more cameras in stereoscopic alignment, a telecentriccamera including a second off-axis camera, or a defocusing system withtwo or more apertures. The imaging apparatus can further include variousfeatures such as off-axis cameras with Scheimpflug angles, foldedoptics, extended depth of field diffraction lenses, or custom optics tomatch the eye curvature of the focal plane or to minimize changes inscale due to camera position. In some embodiments, the light apparatuscan also include various light sources such as, e.g., LEDs, a laser, ora filtered broadband light source.

FIGS. 2A and 2B are, respectively, a photograph and a perspective viewof one embodiment of imaging apparatus 102 that can be implemented withsystem 100 for measuring IOP. According to some embodiments, imagingapparatus 102 can include a first camera 101A and a second camera 101Baligned in a stereoscopic configuration. As can be seen in FIG. 2A,imaging apparatus 102 can also include light source 101 configured toilluminate the eye for purposes of digital imaging. Light source 103 canhave a wavelength of 300-1100 nm. In some embodiments, light source 103can have a wavelength ranging from 350 nm to 450 nm to achieve a highercontrast due to the peak absorption of hemoglobin around a wavelength of400 nm, while avoiding the blue exhaustion band of 450 nm to 480 nm. At400 nm, the blue light safety hazard coefficient is 10% of the peak at460 nm, allowing for brighter lights and longer exposures. The contrastafforded at 400 nm allows for imaging finer blood vessels such as thosenear and around the cornea, which normally would not be visible with aregular imager and illumination. Although FIG. 2A shows an imagingapparatus 102 with light-emitting diodes (LEDs), light source 103 canalso include a laser or a filtered broadband light in addition to orinstead of the LEDs. In some embodiments, imaging apparatus 102 can alsoinclude a light source configured to generate light at near-infrared(IR) wavelengths for imaging the iris. In some embodiments, the lightsource can have a wavelength ranging from 395-405 nm as certain LEDs areconfigured to emit light over a certain spectrum width.

Calibration

With respect to IOP, the current gold standard for measuring IOP is theGoldmann Applanation Tonometry (GAT), which uses a small medical probeto contact the cornea with a variable force to create a defined area ofapplanation. This force is then related to the pressure within the eyethrough the following equation,P=F/A+M−N  (1)where P is the pressure inside the eye, F the force applied, A the areaof applanation, M the surface tension caused by the tear film, and N isthe reaction force of the cornea. For GAT to work, several assumptionsregarding the eye's geometrical and viscoelastic properties were made.For example, the scleral rigidity and ocular size are assumed to beconstant amongst patients. This was necessary to have a universalrelationship for pressure to volume conversions. However, theseassumptions proved to be an oversimplification of the complex nature ofthe ocular system. During aging and onset of ocular diseases, thearchitecture and material properties of the eye are altered, deviatingfrom the GAT assumptions, which manifested in high interpatientvariability and IOP measurement inaccuracy. Corrections factors havebeen proposed to account for the shortcomings of the GAT system.However, GAT is still inherently in accurate and is a contact test,which can be very uncomfortable and inefficient. The disclosed systemsand methods for measuring IOP uses a calibration approach that addressesall of aforementioned shortcomings of GAT.

To better understand the disclosed calibration method of system 100, anoverview of the eye and cardiovascular system is provided. In general,the pulsatile pressure wave generated by the human heart propagatesthroughout the cardiovascular system. These waves reach the eye througharterial vessels resulting in periodic IOP oscillations. The harmoniccomponent of IOP causes volumetric fluctuations in the human eye. Themagnitude of this oscillation depends on the hemodynamic and materialproperties of the sclera. In general, aging causes important changes inthe properties of the sclera which are patient specific. Variousparameters including but not limited to sclera thickness, elasticity andmean IOP define the ocular response to fluctuating pressure forces.Following this reasoning, extracting information about materialproperties from the ocular pulse waveform is an important objective inperforming patient specific measurements.

Exploiting the same conceptual idea as in applanation tonometry, theforce generated by the incoming pressure wave can be used to obtainpatient-specific information and thus measure the intraocular pressuremore accurately without the need to contact the surface of the eye. Inthis respect, the IOP fluctuations can be interpreted as stressvariations on the eye. The IOP can be taken as force per unit areaexerted on the sclera. Thus, the volumetric expansion of the eye isdirectly related to force exerted through IOP and the wall's materialproperties through resistance to deformation. Accordingly, the magnitudeof the IOP inside the eye can be calculated by measuring andcharacterizing the ocular response to an incoming pressure wave.

FIGS. 3A and 3B illustrate examples calibration models 300 and 350,respectively, in accordance with some embodiments of the presentdisclosure. FIG. 3A illustrates a Kelvin solid model for ocularviscoelastic and flow resistance properties. FIG. 3B illustrates asimplified Kelvin model 350 of model 300 shown in FIG. 3A. Given theviscoelastic properties of the sclera, model 300 includes springs anddashpots to mimic the ocular response to an incoming pressure wave.Model 300 is a mathematical model developed to analyze the behavior ofthe sclera under stress. Although model 300 can be used to collectmultiple measurements at a single time point, a single value measurementcan be performed. To this end, model 300 assumes IOP is the only forceinducing stress on the sclera. In this representation, the spring (S1)and dashpot (D1) are placed in parallel to mimic the materialviscoelastic properties, and the spring (S2) represents the flowresistance. While model 300 characterizes the overall stress-strainresponse of the eye, further simplification can help derive a morepractical pressure-strain relationship. For this calibration, the strainresponse of the eye to the incoming pressure wave is characterized. Toachieve this objective, a further simplified model 350 is developed,which also mimics the viscoelastic strain response of the sclera. Bothmodels rely on the eye having an elastic and linear response tophysiological stresses, and not undergoing deformation duringhysteresis. Similar assumptions are already used in the clinic forapplanation tonometry.

The governing equation for model 350 presented relates pressure tostrain in the following relationship,

$\begin{matrix}{{p(t)} = {{D\;{ɛ(t)}} + {E\frac{d\;{ɛ(t)}}{dt}}}} & (2)\end{matrix}$

Where p is the IOP, ε is the strain, D is the elasticity constant and Eis the retardation time constant.

To mathematically analyze a single IOP fluctuation, a reference framefor the system is necessary where time zero will be set at the instancein which the sclera begins a positive volumetric expansion (see to inFIG. 4B). Model 350 includes two parameters that characterize theviscoelastic response of the sclera to the pressure wave. These are themaximum strain change and the delay of maximum strain change from timezero. The maximum strain change will be an indicative measure of thesclera's stiffness. The delay of maximum strain change from time zero isan approximate measure of the retardation time. An example of how toextract these parameters from the ocular pulse wave has been shown below(see FIG. 4B). The pulse waveform can be measured using the methoddescribed in U.S. Publication No. 2017/0209046, which is incorporatedherein in its entirety. The described method allows multiple strainmeasurements to be measured from a single timepoint.

With the patient specific ocular parameters obtained from the pulsewaveform, the mathematical model can be used to relate the strain topressure. The retardation time will be a function of the differencebetween ti and to (see FIG. 4B). In some embodiments, ti is the momentof maximum slope. The elastic modulus will be a function of the maximumstrain change and the retardation time. The parameters obtained from theocular pulse wave will require adjustment with patient data to beimplemented in the proposed model to calculate IOP. Model 350 can beused to infer patient specific parameters that can be used to calculatethe IOP magnitude. More complex model can be also developed to usepulsatile IOP for the same purpose. This patient specific IOPfluctuations can also be used for diagnostic purposes not limited toglaucoma.

For IOP applications, system 100 can be calibrated using deformationdata to fit a displacement model. For heart data calibration such assystolic/diastolic, relative pressure changes due to systolic/diastolicchanges from heart beats can be measured as systolic/diastolic changescan causes variations in IOP. This would allow a single pointcalibration.

For pulse waveform calibration, the estimated radius of curvature of theeye along with the rate of outflow can be used for calibration. Thisallows a single point measurement to infer the Young's modulus of theeye. For example, the Young's modulus can be calculated by measuringlocal changes such as wave propagation through the eye using imagescaptured at a high frame rate.

To calibrate system 100 for measuring material properties, local wavepropagations and bulk parameters such as radius of curvature, bloodpressure, and axial curvature can be used to estimate a calibrated modelwithout a reference calibration.

Example Embodiments of Methods for Measuring Intraocular Pressure

FIG. 5 illustrates a process 500 for calculating IOP of a patient inaccordance with some embodiments of the present disclosure. Process 500starts at 502 where a dynamic digital image of the eye is obtained usingan imaging system such as system 100. Here, a plurality of images of theeye can be taken of the eye to be used for stereoscopic3D-reconstruction at 504.

At 506, keypoint and/or pattern recognition of the blood vessels pattern(BVP) is performed using features recognition and matching algorithmsuch as SIFT or SURF. At 508, ocular pressure pulse waveform isreconstructed by measuring and tracking one or more BVPs (see FIG. 4A).At 510, the viscoelastic properties (e.g., elastic modulus, retardationtime) are measured from the ocular pulse (see FIG. 4B). At 512, the IOPpressure can be derived using the ocular strain and pressurerelationship described by equation (2).

FIGS. 6A to 6D are flow diagrams depicting example embodiments ofmethods for measuring IOP, any of which can be implemented using thesystems and apparatuses described with respect to FIGS. 1, 2A, 2B, 3, 4,5 , and elsewhere throughout the present disclosure. As an initialmatter, those of skill in the art will understand that the method stepsdisclosed herein can comprise instructions stored in non-transitorymemory of a local computing device, mobile computing device, and/orremote server, and that the instructions, when executed by one or moreprocessors of the local computing device, mobile computing device, orremote server, can cause the one or more processors to perform any orall of the method steps disclosed herein. Furthermore, those of skill inthe art will appreciate that any or all of the method steps disclosedherein can be performed by a single device (e.g., local computingdevice) or, in the alternative, can be performed across various devicesin geographically dispersed locations.

Turning to FIG. 6A, a flow diagram depicts an example embodiment of amethod 600 for measuring IOP. In the same seating or setting as theremaining method steps, or in a prior appointment with anophthalmologist, as indicated by box 601, a set of calibration data(e.g., typically a minimum of two points for each eye of a givenpatient) is obtained at Steps 602, 604, 606, 608, and 610.

More specifically, at Step 602, standard ocular tonometry of a patient'seye (or eyes) can be performed using pressure modifying eye-drops.Subsequently, at Step 604, the sclera of the eyes can be digitallyimaged (e.g., by digital photography or videography) using any of theimaging apparatuses described with respect to FIGS. 2A, 2B, 5 , andelsewhere throughout the present disclosure. In some embodiments, eacheye may be imaged in one scene or frame. In other embodiments, multiplescenes or frames may be captured to allow an imaging apparatus with asmaller field of view to interrogate the entire eye. According to oneaspect of these embodiments, the imaging apparatus can be configured tocapture an image of a blood vessel pattern (BVP) against the contrast ofthe white sclera.

Subsequently, at Step 606, a two-dimensional (2D) or three-dimensional(3D) image reconstruction can then be generated based on the capturedimages. In some embodiments, if multiple time points or frames of theeye are captured, a 2D or 3D “reconstruction” can be produced by“stitching” together the images. For 3D-reconstruction, image processingusing defocusing, blur-based imaging or stereo imaging can be employedto form a 3D model. In 2D or 3D, the 3D-reconstruction process may alsoor alternatively involve creating identifiable keypoints (such as ORB,SIFT, SURF, or neural network-derived features) involving orcharacterizing the included blood vessel patterns contrasted by oragainst the sclera. When capturing subsequent images by each camera,information of spatially correlated image patches can be combined tode-noise and enhance the measurements. This method can also be used tocreate higher resolution models of those patches for more accurate IOPdetermination.

At Step 608, keypoint and/or pattern recognition of the BVP (e.g., byusing SIFT software or other feature matching and stitching algorithms)may be performed. Together, at Step 610, these data are variouslyanalyzed (i.e., all such data may be used or only a subset thereof used)to establish a pressure-BVP-size relationship. These steps (optionallytaken together and performed in a separate procedure, as indicated bythe box indicating Step 601) produce a calibration data set relating eyepressure to the BVP across the sclera of at least one human eye.

After producing the calibration data set, one or both eyes are digitallyre-imaged or imaged for pressure measurement at Step 612 (through thesame or similar methods described with respect to Step 604). This stepmay be performed in the same setting or session (e.g., once a patient'socular pressure is no longer influenced by the use of drops, orotherwise). Alternatively, it may occur at a later date in one or morefollow-up visits to a clinic or other outpatient setting. Generally,such visits will be separated in time on the order of several months. Inthe case of monitoring for IOP, yearly changes associated with volumeand/or BVP scale change (in which no calibration data is produced perStep 610), or in the full method in which calibration data is employedmay be observed over a patient's lifetime.

At Step 614, a 2D or 3D image reconstruction is again performed based onthe image(s) captured at Step 612. Then, at Step 616, patternrecognition techniques can again be applied to the 2D or3D-econstruction characterizing the BVPs. At Step 618, keypoints and/orpattern matching can be performed and compared to that produced oravailable from the calibration data set produced through the steps shownin box 601. According to one aspect of the embodiments, the comparison(or comparisons, if additional scans are made of the eye after initialcalibration data scanning) yields an estimation of scale change.

Together with the pressure-based calibration data, an estimatedmeasurement of IOP can be produced as an output at Step 620.Alternatively, in some embodiments, a comparison of overall volumechange (eliminating the need for various keypoints or patternrecognition) and/or scale change BVP features can be made without usingcalibration data. In some embodiments, for example, the comparison at618 can be between an earlier image data set and a current or laterimage data set, and the output at 620 may be a relative indication ofchange in IOP (e.g., as an alert regarding climbing or escalating IOP).Any such, output may be visually displayed on a monitor of such hardwareas referenced earlier, stored in a patient file or database, orotherwise handled.

Turning to FIG. 6B, a flow diagram depicts another example embodiment ofa process 630 for measuring IOP. Similar to method 600, as indicated bybox 631, a set of calibration data is obtained at Steps 632, 634, 636,638, and 640. More specifically, at Step 632, standard ocular tonometryof a patient's eye (or eyes) can be performed using pressure modifyingeye-drops. Subsequently, at Step 634, a 3D image of the eye (or eyes) iscaptured using an imaging apparatus. According to some embodiments, theimaging apparatus can be configured to utilize stereoscopic and/ordefocusing techniques to capture a 3D image of the surface of the scleraof each eye.

Subsequently, at Step 636, a 3D image 3D-reconstruction can then begenerated based on the captured image. According to some embodiments,image processing using defocusing, blur-based imaging, and/or stereoimaging can be employed to form a 3D model. At Step 638, a first set ofcurvature metrics can be extracted from the generated 3D image. In someembodiments, the curvature metrics can include, for example, a meanradius of the sclera. In other embodiments, curvature metrics can alsoinclude the local surface curvatures at every point of the 3D image,such as the standard principal curvatures that can be computed from a 3Dmesh. Then, at Step 640, these data are variously analyzed to establisha relationship between the curvature metrics and IOP.

After producing the calibration data set, another 3D image of the eye(or eyes) is captured by the imaging apparatus at Step 642. This stepmay be performed in the same setting or session (e.g., once a patient'socular pressure is no longer influenced by the use of drops, orotherwise) or, alternatively, at a later date in one or more follow-upvisits to a clinic or other outpatient setting. At Step 644, a 3D imagereconstruction can then be generated based on the image captured at Step642, and a second set of curvature metrics can be extracted from thegenerated 3D image at Step 646.

Subsequently, at Step 648, the second set of curvature metrics can becompared to the first set of curvature metrics to determine anestimation of scale change. Utilizing the IOP-curvature relationshipdetermined at Step 640, an estimated measurement of IOP can bedetermined and output to a display at Step 650. In some embodiments, thefirst and second sets of curvature metrics and IOP measurements can alsobe stored in a patient file or database. Moreover, those of skill in theart will appreciate that Steps 642, 644, 646, 648, and 650 of method 630can be iteratively repeated, at a later date, to generate additionalsets of curvature metrics (to be compared with earlier sets of curvaturemetrics), in order to track change in a patient's IOP for purposes ofdiagnosing, monitoring, and treating the progression of an adversecondition such as glaucoma.

Turning to FIG. 6C, a flow diagram depicts another example embodiment ofa process 660 for measuring IOP. Similar to processes 600 and 630, asindicated by box 661, a set of calibration data can first be obtained atSteps 662, 664, 666, 668, and 670. More specifically, at Step 662,standard ocular tonometry of patient's eye (or eyes) can be performedusing pressure modifying eye-drops. Subsequently, at Step 664, a 3Dimage of the eye (or eyes) is captured using an imaging apparatus. Inmany of the embodiments, the imaging apparatus can comprise at least onetelecentric lens. Similarly, the imaging apparatus can be configured toutilize stereoscopic and/or defocusing techniques to capture a 3D imageof the surface of the sclera of each eye and/or further configured tocapture an image of a blood vessel pattern (BVP) against the contrast ofthe white sclera. Furthermore, the upper layers of the sclera areattached to muscle and eye movement can cause inaccuracies in themeasurements described below. Because the area around the cornea is lessaffected by muscle strain on the eye, in some embodiments, the imagingapparatus can be positioned and/or configured to image the patient'ssclera near or around the cornea.

Subsequently, at Step 666, a 3D image reconstruction can then begenerated based on the captured 3D image. According to some embodiments,image processing using defocusing, blur-based imaging, or stereo imagingcan be employed to form a 3D model. At Step 668, point pairs of similarelevation are identified in the 3D model and the distances between thepoints are determined. As described in further detail below, tests showthat by using point pairs of similar elevation, distance measurement ismore accurate because point x-y localization is generally more accuratethan points having different elevations (i.e., along the z-axis). AtStep 670, these data are variously analyzed to establish a relationshipbetween the point pair distance and IOP.

After producing the calibration data set, another 3D image of the eye(or eyes) is captured by the imaging apparatus at Step 672. This stepmay be performed in the same setting or session (e.g., once a patient'socular pressure is no longer influenced by the use of drops, orotherwise) or, alternatively, at a later date in one or more follow-upvisits to a clinic or other outpatient setting. At Step 674, a 3D imagereconstruction can then be generated based on the image captured at Step672, and a second set of measured distances between point pairs ofsimilar elevation can be determined at Step 676.

Subsequently, at Step 678, the second set of measured distances betweenpair points of similar elevation can be compared to the first set ofmeasured distances to determine a net change of the surface of thesclera. According to some embodiments, this can be determined byutilizing measurement covariance, either through direct calculation ornumerical approximation. Utilizing the point-pair-distance-to-IOPrelationship determined at Step 670, an estimated measurement of IOP canbe determined and output to a display at Step 680. In some embodiments,the first and second sets of measured distances and IOP measurements canalso be stored in a patient file or database. Moreover, those of skillin the art will appreciate that Steps 672, 674, 676, 678, and 680 ofmethod 660 can be iteratively repeated, at a later date, to generateadditional sets of measured distances between point pairs of similarelevation (to be compared with earlier sets of data), in order to trackchange in a patient's IOP for purposes of diagnosing, monitoring, andtreating the progression of an adverse condition such as glaucoma.

Turning to FIG. 6D, a flow diagram depicts an example embodiment of aprocess 690 for using IOP measurements for aiding in a medicaldiagnosis. According to one aspect of the embodiments, process 690operates on the principle that IOP naturally varies with changes inblood pressure caused by the heartbeat. By using captured video imagedata of the eye as a method for calibrating the relative rate ofexpansion of the eye relative to IOP, a rate-of-linear-change toIOP-change relationship can be determined and subsequent IOP changes canbe measured utilizing the imaging techniques described herein.

Referring still to FIG. 6D, at Step 691, digital images of the scleraare captured using an imaging apparatus comprising one or morehigh-speed camera sensors. In some embodiments, the imaging apparatuscan capture multiple 3D images in rapid succession and/or video of thesclera of the patient's eye (or eyes). In other embodiments, the imagingapparatus can be configured to capture video of the sclera at a rategreater than 30 Hz. Still referring to Step 691, while the 3D imagesand/or video are being captured, synchronous blood pressure measurementsare taken. In addition, a standard tonometry can be performed withoutthe use of pressure-modifying eye drops to obtain a reference IOPmeasurement. At Step 692, a 3D image 3D-rerconstruction is generatedfrom which blood vessel patterns (BVPs) can be identified and curvaturemetrics of the sclera can be determined. At Step 693, using thesynchronous blood pressure measurements and the 3D image reconstruction,deformation between the areas of BVPs between a first blood pressurevalue (e.g., a nadir or diastolic blood pressure value) and a secondblood pressure value (e.g., a peak or systolic blood pressure value) canbe measured. At Step 694, based on the deformation measurement, thereference IOP measurement, and the curvature metrics, a relationshipbetween a rate-of-linear-change relative to IOP-change can then bedetermined. In subsequent sessions, as shown at Step 697, relative IOPchanges in the patient's eye can be determined by re-imaging the scleraand applying the relationship between the rate-of-linear-change toIOP-change. The IOP measurements generated at Step 697 can then beoutput to a display. Optionally, in some embodiments, at Step 695, therelative IOP changes can be combined with other physiologicalmeasurements (e.g., other parts of the body). At Step 696, a medicaldiagnosis can be generated based on the combination of the IOPmeasurement(s) and the physiological measurements.

In some embodiments, IOP can be determined by comparing the dilation ofblood vessel positions (or other eye features) at different time points.For example, process 690 can construct a 3D model of a portion of theeye and monitor that particular portion at 1 Hz to measure the changesin dilation. Process 690 can also track the variations in dilation asthe blood vessels pulsate and measure the average dilation over acertain period of time, which can then be used to measure IOP. Process690 can also measure IOP based on at least the resistance to pressure bythe scleral wall of the eye and outflow resistance of the vasculature.

According to another aspect of the embodiments, encryption can beutilized to secure any of the data acquired or transmitted by any of thesystems, devices, and apparatuses described herein, including any of thephysiological measurements (including, but not limited to, IOPmeasurements and blood pressure measurements), medical diagnoses, imagescaptured by the imaging apparatus, image reconstructions, BVPmeasurements (including but not limited to point pair distances),curvature metrics, deformation values, rate-of-linear-change toIOP-change relationships, and the like. Those of skill in the art willalso appreciate that such data can be encrypted in storage or intransit, for example, through the use of public and private keys, or anydesired technique or scheme (e.g., key generation algorithms, signingalgorithms, and signature verifying algorithms). These and othersuitable examples include, but are not limited to, techniques or schemesbased on the RSA algorithms (and their variants), El Gamal algorithms(and their variants), Diffie-Hellman algorithms, Digital SignatureAlgorithm (DSA) and its variants, elliptical curve-based algorithms andits variants, and/or Rabin algorithms and its variants.

Those of skill in the art will also appreciate that any of the methodsteps described with respect to methods 600, 630, 660, and 690 arefreely combinable with any of the other method steps described withinthe present disclosure to achieve the result of an intraocular pressuremeasurement. Likewise, FIGS. 6A to 6D illustrate example embodiments ofmethods for measuring intraocular pressure and are not intended to limitthe order in which the steps are performed, as those of skill in the artwill appreciate.

Experimental Data

Experiments performed in relation to the embodiments of the presentdisclosure will now be described. FIGS. 7A and 7B are, respectively, aperspective view and a photograph of an inflatable model 700 of an eyeused for testing an example embodiment of a system for measuring IOP.According to one aspect of the testing, a precision stage was utilized,wherein a target was shifted in 5 um increments while being imaged by animaging apparatus. In this case, the imaging apparatus comprised twocameras in a stereoscopic alignment with multiple LEDs for providing alight source. Subsequently, key points were extracted and filtered by aquality metric obtained from the images. As can be seen in the testresults in the graphs depicted in FIGS. 7C and 7D, the mean shift errorwas less than 1 um. Furthermore, as shown in the graph in FIG. 7E, thetypical standard deviation of high-quality individual x,y point shifterrors was found to be between 0.5 um and 1.0 um. By contrast, thetypical standard deviation of individual point shift errors around thenoise along the z-axis was found to be between 1.0 um and 5.0 um. Basedon the aforementioned results, it was determined that x,y pointlocalization (i.e., for point pairs having similar elevation) forhigh-quality points yielded relatively accurate measurements.

FIGS. 8A to 8D are, respectively, a photographic image and3D-reconstruction of an actual subject's eye (i.e., not a model)utilized in testing an example embodiment of a system for measuring IOP.Results of the 3D-reconstruction are further shown in FIGS. 8E and 8F,which comprise a ten parameter pseudo-ellipsoid surface fit generatedfrom the captured image of the subject's sclera. The pseudo-ellipsoidsurface comprised over 2,000 points with a standard deviation of lessthan 30 um for high quality points. It is further surmised that theerror is lower due to an assumption that the surface is not a perfectrepresentation. Based on the 3D-reconstruction, a curvature integrationover the fit surface was performed by bootstrapping data at a 50%subsampling rate and iteratively refitting the surface. As can be seenin the results in the graphs depicted in FIGS. 8G and 8H, the meanradius from the integrated curvature was determined to be 12.55 mm witha 0.037 standard deviation.

FIG. 9A is a photograph of another inflatable model 900 utilized intesting measurements of surface deformations by an example embodiment ofa system for measuring IOP. According to one aspect of the experiment,model 900 was connected to a regulated air pressure system and run atthe lowest possible increments. Deformation measurement results can beseen in the graph depicted in FIG. 9B, which shows that trackingcorresponds to accuracies of better than 10 um per 12 mm. Surfacechanges were measured at approximately 0.32 um/mmHG per mm. It wasdetermined that a uniform sphere of the tested material having a 12 mmradius would change at 3.8 um/mmHG. It was further noted that elasticityof the eye is substantially larger (e.g., 20 um/mmHG). FIG. 9C is agraph depicting measured scale versus relative pressure for multiplepoints being tracked on the model. According to an aspect of theexperiment, the material and shape used for the model was non-uniform,and thus it was surmised that deformations would not be homogeneous. Ascan be seen in the graph depicted in FIG. 9C, different tracked pointsshow strong consistency in relative scale differences. From the results,it was concluded that the example embodiment of the system for measuringIOP would be capable of working with non-uniform surface deformations.

Curvature and Volumetric Measurement

FIG. 10 illustrates a process 1000 for measuring POBF in accordance withsome embodiments of the present disclosure. At the sub-process shown inFIG. 10A, the local curvature and volume of the eye over time ismeasured and tracked based at least on images having depth information,which are captured using high-speed imaging system 102 (see FIG. 1 ).The captured images can be used to generate a 3D model of the eye. Insome embodiments, local curvature and volume metric measurement scan beobtained by analyzing the 3D model at a single time point and/or overmultiple time points (see FIG. 10B).

For example, imaging system 102 can focus on certain portion of the eyeto measure the local curvature variation with time for each ocularpressure pulse wave. The measured local curvature would then be used toreconstruct the volumetric structure of the eye at every instance of therecording (FIG. 10B). To reconstruct the volumetric structure multiplemethodologies can be used. The volumetric pulsations can then bedirectly used to measure the net flow profile during the pulse. Theconstant outflow behavior of the eye can be approximated by analyzingthe information in multiple pulses. With the assumption of the constantoutflow, the POBF can be reconstructed for each individual pulse. Toreconstruct the pulse with enough detail, it is clinically recommendedto sample at a frequency of at least one hundred hertz.

At the sub-process shown in FIG. 10C, a volumetric pulsation iscalculated by observing the changes in volume of one or more regions ofthe eye (e.g., blood vessel region) over a period of time. At thesub-process shown in FIG. 10D, the volumetric measurement is convertedto a flow measurement, which can be used to estimate the total amount offluid that enters the eye in a given period of time. In FIG. 10E, singlepulse is mathematically analyzed. Given the nature of the pulsation, aconvenient reference frame is marked from the start of the volumetricexpansions. A single pulse would consequently terminate at the beginningof the new volumetric expansion. This definition is of particularimportance when analyzing individual pulses for POBF.

In some embodiments, the pulsatile ocular blood flow can be measured byassuming a constant volume over time. Alternatively, the IOP can bemeasured from the ocular pulsation (see FIG. 4B). Next, the constantoutflow can be calculated using the measured IOP and a constant volumeover time assumption. Finally, the pulsatile ocular blood flow can bedetermined based on the constant outflow calculation.

It should be noted that ocular volumetric pulsations are a measurableoutcome of fluid-solid interactions in the eye. With the application ofmachine learning applied to the measured curvature signal, furtherinsight about ocular health can be obtained. Additional parameters notlimited to POBF can be extracted from ocular local curvature variationover time. For diagnostic purposes, applying machine learning trendrecognition and categorization on multiple measurable parameters canlead to a more favorable prognosis. To this end, process 1000 can beapplied to any of the following but is not limited to: diabeticretinopathy, age related macular degeneration, carotid artery stenosis,and arrythmias.

Example Embodiment of Local Computing Device

FIG. 11 is a block diagram depicting an example embodiment of localcomputing device 106, which can include one or more processors 1120coupled to memory 1130, communications circuitry 1140, storage device1150, input devices 1160, power management module 1170, and outputdevices 1180. Processors 1120 can include, for example, ageneral-purpose central processing unit (“CPU”) 1123, a graphicsprocessing unit (“GPU”) 1125, an application-specific integrated circuit(“ASIC”), a field programmable gate array (“FPGA”), or any other type ofprocessor, e.g., Application-specific Standard Products (“ASSPs”),Systems-on-a-Chip (“SOCs”), Programmable Logic Devices (“PLDs”), andother similar components. Processors 1120 can include one or moreprocessors, microprocessors, controllers, and/or microcontrollers, eachof which can be a discrete chip or distributed amongst (and a portionof) a number of different chips, and collectively, can have the majorityof the processing capability for executing instructions stored in memory1130.

Memory 1130 can include volatile memory, including, e.g., high-speedrandom access memory such as DRAM, SRAM, DDR RAM, and/or non-volatilememory, e.g., ROM, EEPROM, flash memory, or a combination thereof thatis accessible by the one or more processors 1120. Memory 1130 can beused to store instructions, for example, comprising software toimplement the steps of the embodied methods disclosed herein. Similarly,storage device 1150 can include computer readable storage medium forstoring instructions comprising software to implement the steps of theembodied methods disclosed herein. For example, storage device 1150 caninclude one or more of magnetic disk storage devices (e.g., hard diskdrives (“HDDs”)), optical disk storage devices, Blu-Ray BD-ROMs, DVDs,CD-ROMs, flash memory devices or other non-volatile solid-state storagedevices.

Referring still to FIG. 11 , communications circuitry 1140 can includeany transceiver-like mechanism that enables local computing device 106to communicate with other devices and/or systems through an electrical,optical, RF or other carrier medium. Communications circuitry 1140 canalso include either or both wireless and wired network interfaces. Insome embodiments, for example, communications circuitry 1140 can includeone or more ethernet ports, an 802.11x wireless network (also referredto as “Wi-Fi X”) port, and a Bluetooth or Bluetooth Low Energy (“BLE”)wireless link port capable of supporting multiple Bluetooth and/or BLEconnections. As can be seen in FIG. 11 , communications circuitry 1140can also be coupled to an antenna 1145.

According to another aspect of the disclosed embodiments, local computerdevice 106 can also include a power management module 1170 for managingand conserving power for the various components of local computingdevice 106; output devices 1180 including display 112, projectors,printers and speakers (not shown); as well as input devices 1160including imaging apparatus 102, keyboards and/or mice 1114, trackpads,touchpads, touchscreens, microphones, voice recognition devices,biometric devices and any other external and/or peripheral deviceadapted for receiving input from a user. As understood by one of skillin the art, all of the aforementioned components are electrically andcommunicatively coupled in a manner to make one or more functionaldevices.

Example Embodiment of Remote Server

FIG. 12 is a block diagram depicting an example embodiment of remoteserver 108. As shown in the diagram, remote server 108 can include anoutput/display component 1275, one or more processors 1255, memory 1260,including non-transitory memory, RAM, Flash or other types of memory,communications circuitry 1270, which can include both wireless and wirednetwork interfaces, mass storage devices 1265, and input devices 1280,which can include keyboards, mice, trackpads, touchpads, microphones,and other user input devices. The one or more processors 1255 caninclude, for example, a general-purpose CPU, a GPU, an ASIC, an FPGA,ASSPs, SOCs, PLDs, and other similar components, and furthermore, cancomprise one or more processors, microprocessors, controllers, and/ormicrocontrollers, each of which can be a discrete chip or distributedamongst (and a portion of) a number of different chips. As understood byone of skill in the art, these components are electrically andcommunicatively coupled in a manner to make a functional device.

Referring still to FIG. 12 , remote server 108 can include database 1268for storing image data or processed image data received from localcomputing device 106, imaging apparatus 102, and/or mobile computingdevice 110. In some embodiments, database 1268 can be part of a storagearea network, for example, to which remote server 108 is communicativelycoupled. In many embodiments, communications circuitry 1270 can includea single network interface, either wired or wireless; or, in otherembodiments, communications circuitry 1270 can include multiple networkinterfaces, either wired or wireless, to provide for enhanced security,monitoring and traffic shaping and management.

Example Embodiment of Mobile Computing Device

FIG. 13 is a block diagram depicting an example embodiment of a mobilecomputing device 120, which can be, e.g., a smartphone, PDA, or tabletdevice. Mobile computing device 120 includes an imaging module 1326coupled to one or more cameras 1327, wherein imaging module 1326 isconfigured to cause the one or more cameras 1327 to capture a digitalimage of the patient's eye. Additionally, mobile computing device 120can include a display 122, input component 1321, and a processing core1306 including a communications processor 1322 coupled with memory 1323and an applications processor 1324 coupled with memory 1325. Alsoincluded can be separate memory 1330, RF transceiver 1328 with antenna1329, and power supply and power management module 1338. Further, mobilecomputing device 120 can also include a multi-functional transceiver1332 which can communicate over Wi-Fi, NFC, Bluetooth, BTLE, andcellular networks with an antenna 1334. As understood by one of skill inthe art, these components are electrically and communicatively coupledin a manner to make a functional device.

ADDITIONAL EMBODIMENTS

In many embodiments, a system for measuring intraocular pressure of apatient's eye is disclosed. The system can include an imaging apparatusconfigured to capture three-dimensional (3D) images of a sclera of thepatient's eye; one or more processors communicatively coupled with theimaging apparatus; and non-transitory memory coupled to the one or moreprocessors. The non-transitory memory can store instructions that, whenexecuted by the one or more processors, cause the one or more processorsto: cause the imaging apparatus to capture a first set of 3D images ofthe sclera, receive a reference IOP measurement and a plurality of bloodpressure measurements synchronously taken with the first set of 3Dimages, wherein the plurality of blood pressure measurement includes afirst blood pressure value and a second blood pressure value, generate afirst 3D image 3D-reconstruction of the sclera based on the first set of3D images, identify blood vessel patterns (BVPs) in the first 3D image3D-reconstruction and extract a first set of curvature metrics of thesclera, determine a first deformation value between the first bloodpressure value and the second blood pressure value, and determine arate-of-linear-change to IOP-change relationship based on the referenceIOP measurement, the first deformation value, and the first set ofcurvature metrics. The instructions can further include instructions,when executed by the one or more processors, further cause the one ormore processors to: cause the imaging apparatus to capture a second setof 3D images of the sclera, generate a second 3D image reconstruction ofthe sclera based on the second set of 3D images, identify the BVPs inthe second 3D image reconstruction and extract a second set of curvaturemetrics of the sclera, determine a second deformation value based on acomparison of the BVPs in the first and the second 3D imagereconstruction, and determine an intraocular pressure measurement basedon the second deformation and the rate-of-linear-change to IOP-changerelationship.

The system can display the output the intraocular pressure measurementon a display. The intraocular pressure measurement can be measured as achange in intraocular pressure. The reference IOP measurement is takenby a standard ocular tonometry of the patient's eye without usingpressure-modifying eye drops.

The imaging apparatus can have two or more cameras in a stereoscopicalignment. The imaging apparatus can also have a telecentric lens with asecond off-axis camera. The imaging apparatus can include a defocusingsystem with two or more apertures. Additionally, the imaging apparatuscan include one or more off-axis cameras having Scheimpflug anglesconfigured to give aligned focal planes. The imaging apparatus can alsoinclude folded optics and/or custom optics made to match an eyecurvature of a focal plane. The imaging apparatus can further include anf-theta lens. The imaging apparatus can also include a light source thatgenerates light at a wavelength between 350 nm and 450 nm. The lightsource can include one or more of a light-emitting diode (LED), a laser,and a filtered broadband light source. The imaging apparatus isconfigured to capture video of the sclera at a rate greater than 30 Hz.

In some embodiments, the instructions to determine the first and thesecond deformation values include measuring distances of areas betweenBVPs and/or measuring distances between point pairs of similarelevation.

The first blood pressure value can be a diastolic blood pressure valueand the second blood pressure value can be a systolic blood pressurevalue.

The instructions, when executed by the one or more processors, canfurther cause the one or more processors to: combine the intraocularpressure measurement with one or more physiological measurements, andgenerate a medical diagnosis based on the combined intraocular pressuremeasurement and the one or more physiological measurements.

The instructions, when executed by the one or more processors, canfurther cause the one or more processors to encrypt and store in thenon-transitory memory acquired data that can include one or more of: thefirst and the second sets of 3D images, the reference IOP measurement,the plurality of blood pressure measurements, the first and the second3D image reconstructions, the first and the second set of curvaturemetrics, the first and the second deformation values, therate-of-linear-change to IOP-change relationship, or the intraocularpressure measurement.

It should be noted that in any of the aforementioned processes (e.g.,processes 500, 600, 630, 660, 690), any of the sub-processes or stepscan be performed out of order or be omitted entirely. Additionally, anyof the sub-processes can be performed automatically and/or be combinedwith one or more other sub-processes.

Throughout this disclosure, the preferred embodiment and examplesillustrated should be considered as exemplars, rather than aslimitations on the present inventive subject matter, which includes manyinventions. As used herein, the term “inventive subject matter,”“system,” “device,” “apparatus,” “method,” “present system,” “presentdevice,” “present apparatus” or “present method” refers to any and allof the embodiments described herein, and any equivalents.

It should also be noted that all features, elements, components,functions, and steps described with respect to any embodiment providedherein are intended to be freely combinable and substitutable with thosefrom any other embodiment. If a certain feature, element, component,function, or step is described with respect to only one embodiment, thenit should be understood that that feature, element, component, function,or step can be used with every other embodiment described herein unlessexplicitly stated otherwise. This paragraph therefore serves asantecedent basis and written support for the introduction of claims, atany time, that combine features, elements, components, functions, andsteps from different embodiments, or that substitute features, elements,components, functions, and steps from one embodiment with those ofanother, even if the following description does not explicitly state, ina particular instance, that such combinations or substitutions arepossible. It is explicitly acknowledged that express recitation of everypossible combination and substitution is overly burdensome, especiallygiven that the permissibility of each and every such combination andsubstitution will be readily recognized by those of ordinary skill inthe art.

When an element or feature is referred to as being “on” or “adjacent” toanother element or feature, it can be directly on or adjacent the otherelement or feature or intervening elements or features may also bepresent. In contrast, when an element is referred to as being “directlyon” or extending “directly onto” another element, there are nointervening elements present. Additionally, when an element is referredto as being “connected” or “coupled” to another element, it can bedirectly connected or coupled to the other element or interveningelements may be present. In contrast, when an element is referred to asbeing “directly connected” or “directly coupled” to another element,there are no intervening elements present.

Furthermore, relative terms such as “inner,” “outer,” “upper,” “top,”“above,” “lower,” “bottom,” “beneath,” “below,” and similar terms, maybe used herein to describe a relationship of one element to another.Terms such as “higher,” “lower,” “wider,” “narrower,” and similar terms,may be used herein to describe angular relationships. It is understoodthat these terms are intended to encompass different orientations of theelements or system in addition to the orientation depicted in thefigures.

Although the terms first, second, third, etc., may be used herein todescribe various elements, components, regions, and/or sections, theseelements, components, regions, and/or sections should not be limited bythese terms. These terms are only used to distinguish one element,component, region, or section from another. Thus, unless expresslystated otherwise, a first element, component, region, or sectiondiscussed below could be termed a second element, component, region, orsection without departing from the teachings of the inventive subjectmatter. As used herein, the term “and/or” includes any and allcombinations of one or more of the associated list items.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a,” “an,” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. Forexample, when the present specification refers to “an” assembly, it isunderstood that this language encompasses a single assembly or aplurality or array of assemblies. It will be further understood that theterms “comprises,” “comprising,” “includes,” and/or “including” whenused herein, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

Embodiments are described herein with reference to view illustrationsthat are schematic illustrations. As such, the actual thickness ofelements can be different, and variations from the shapes of theillustrations as a result, for example, of manufacturing techniquesand/or tolerances are expected. Thus, the elements illustrated in thefigures are schematic in nature and their shapes are not intended toillustrate the precise shape of a region and are not intended to limitthe scope of the inventive subject matter.

The foregoing is intended to cover all modifications, equivalents andalternative constructions falling within the spirit and scope of theinvention as expressed in the appended claims, wherein no portion of thedisclosure is intended, expressly or implicitly, to be dedicated to thepublic domain if not set forth in the claims. Furthermore, any features,functions, steps, or elements of the embodiments may be recited in oradded to the claims, as well as negative limitations that define theinventive scope of the claims by features, functions, steps, or elementsthat are not within that scope.

What is claimed is:
 1. A system for performing contactless measurementof one or more characteristics of a patient's eye used to diagnosis apotential medical condition, the system comprising: a light sourceconfigured to illuminate the eye; a 3D-camera assembly configured tocapture a plurality of images of the eye, wherein the plurality ofimages comprises depth information; a 3D-reconstruction moduleconfigured to generate a 3D model of a portion of the eye based at leaston the plurality of images; and a data analytic module configured todetermine one or more characteristics of the eye based at least on the3D model.
 2. The system of claim 1, wherein the one or morecharacteristics of the eye comprises one or more of blood vesselfeatures, curvature metrics of a sclera of the eye, volumetricpulsations of the eye, total eye volume, deformations, relative localdisplacements over time, rate of local blood outflow, and radius of theeye.
 3. The system of claim 2, further comprising an intraocularpressure (TOP) diagnostic module configured to determine the TOP withinthe eye based at least on the rate of local blood outflow.
 4. The systemof claim 3, further comprising a blood pressure measuring apparatusconfigured to synchronously measure the patient's blood pressure, andwherein the TOP diagnostic module is configured to determine the TOPwithin the eye based at least on the patient's blood pressure and therate of local blood outflow.
 5. The system of claim 2, furthercomprising: a heart monitor configured to obtain heart data of thepatient's heart; and a diagnostic module configured to flag a potentialmedical condition based at least on (a) deformations of the eye and theheart data, (b) relative local displacements over time and heart data,OR (c) variation in the relative local displacements over time and heartdata.
 6. The system of claim 2, further comprising a diagnostic moduleconfigured to determine pulsatile ocular blood flow (POBF) based atleast on the volumetric pulsations of the eye.
 7. The system of claim 1,wherein the 3D-camera assembly comprises a plurality of cameras instereoscopic alignment or a camera configured to capture a plurality ofimages at different focuses.
 8. The system of claim 1, wherein the3D-camera assembly comprises a telecentric camera and an off-axis cameraor one or more off-axis cameras with Scheimpflug angles.
 9. The systemof claim 1, wherein the 3D-camera assembly comprises a light-basedimaging system.
 10. The system of claim 9, wherein the light-basedimaging system comprises one of a RGB-D camera, a light ranging anddetection (LIDAR) system, a structured light system, or a time of flightsystem.
 11. The system of claim 1, wherein the 3D-camera assemblycomprises one or more high-speed cameras configured to capture images ata frame rate between 30-5000 frames per second.
 12. The system of claim1, wherein the light source comprises a wavelength having a rangebetween 350 and 450 nm.
 13. The system of claim 12, wherein the lightsource comprises a wavelength having a range of 395 to 405 nm.
 14. Asystem for identifying potential medical conditions using contactlessmeasurement of one or more characteristics of a patient's eye, thesystem comprising: a light source configured to illuminate the eye; animage capturing assembly configured to capture a plurality of images ofthe eye, wherein the plurality of images comprises depth information; anon-transitory memory configured to store instructions that, whenexecuted by one or more processors, cause the one or more processors to:generate a 3D model of a portion of the eye based at least on theplurality of images; determine one or more characteristics of the eyebased at least on the 3D information, wherein the one or morecharacteristics comprise one or more of blood vessel features, curvaturemetrics of a sclera of the eye, volumetric pulsations of the eye, totaleye volume, deformations, relative local displacements over time, andradius of the eye; and identify a potential medical condition based atleast on the one or more characteristics of the eye.
 15. The system ofclaim 14, wherein the image capturing assembly comprises one or morehigh-speed cameras configured to capture images at a frame rate between30-5000 frames per second or a light-based camera system.
 16. The systemof claim 14, wherein the light source comprises a wavelength having arange between 350 and 450 nm.